Sentiment analysis has been widely applied to on-line posts, such as news, reviews and social media, including comments, and discussions, for different applications. Such applications range from marketing to customer service. In general, sentiment analysis aims to determine attitude or sentiment opinion of a speaker or a writer with respect to some topic.
In numerous cases, posts, particularly with social media and on-line discussions, have multiple topics. For example, an on-line discussion may include multiple topics due to numerous comments from different users, particularly as the discussion progresses. The topics may share different sentiment opinions. For example, comments or posts from different followers may present their sentiment opinions. Furthermore, new topics may be presented by followers. As such, a gathering of posts from one origin may diverge in topics, each of which receives distinct attentions of people, and different topics would have different sentiment polarities.
An important aspect of sentiment analysis is to extract topics and their corresponding sentiment polarities from a series of posts. For example, a news supplier would be interested to learn what news are most favourable or a manufacturer would like to discover the advantages and defects of its products in order to make better products.
The present disclosure relates to a method and system to effectively and efficiently analyze posts, such as documents and related comments to identify multiple topics and their corresponding sentiment polarities.